A cast-in-situ plain concrete wind tunnel body anti-cracking construction method

Abstract

The present application provides a kind of cast-in-place fair-faced concrete wind tunnel body crack resistance construction method, belong to wind tunnel technical field, the present application is by preparing low heat high crack resistance concrete and incorporating basalt fiber and double expansion source expansion agent, pre-embedded cooling water pipe network and temperature monitoring point in tunnel body, according to hydration heat temperature rise control equation dynamic adjustment cooling water flow, using formwork pre-deformation compensation design eliminates forming error, according to the way of layered pouring control temperature rise rate, implement early curing and through shrinkage strain game model optimization curing parameter, after stripping, carry out precision measurement and feedback correction formwork deformation prediction equation, solve the technical problem that cast-in-place fair-faced concrete wind tunnel body is caused by hydration heat temperature rise and produces early crack due to internal and external temperature difference too large.

Description

Technical Field

[0001] This invention belongs to the field of wind tunnel technology, and more specifically, relates to a method for crack-resistant construction of cast-in-place fair-faced concrete wind tunnel bodies. Background Technology

[0002] As a core component of large-scale aerodynamic testing facilities, wind tunnels require extremely high surface precision and integrity. Cast-in-place fair-faced concrete technology is widely used in wind tunnel construction due to its good integrity and continuous surface. Traditional construction methods mainly use ordinary Portland cement to prepare concrete, controlling temperature through natural curing or simple covering curing, and relying on experience to adjust the pouring speed to control the release of heat of hydration. In traditional technology, because the heat of hydration release of large-volume concrete is concentrated and large in total, the temperature of the concrete core often rises rapidly to over 70°C within 24 to 48 hours after pouring, while the surface temperature is relatively lower due to environmental influences, resulting in a temperature difference of over 25°C between the core and surface. This temperature gradient generates significant tensile stress within the concrete. Simultaneously, traditional curing methods struggle to dynamically adjust curing parameters based on actual concrete temperature changes, failing to effectively control the rate of early shrinkage strain development, causing the concrete to bear excessive tensile stress in the early stages of hardening. In other words, existing technologies present a technical problem: early cracking occurs in cast-in-place fair-faced concrete wind tunnels due to excessive internal and external temperature differences caused by the heat of hydration. Summary of the Invention

[0003] In view of this, the present invention provides a method for crack-resistant construction of cast-in-place fair-faced concrete wind tunnels, which can solve the technical problem in the prior art of early cracks caused by excessive internal and external temperature differences due to the heat of hydration in cast-in-place fair-faced concrete wind tunnels.

[0004] This invention is implemented as follows: This invention provides a method for constructing a cast-in-place fair-faced concrete wind tunnel with crack resistance, including preparing low-heat, high-crack-resistant concrete; mixing fly ash and mineral powder to form a dual-admixture system; adding basalt fiber and a dual-expansion-source expansion agent; pre-dispersing the basalt fiber using an airflow dispersion device before feeding the concrete; dry mixing followed by wet mixing; pre-embedding cooling water pipes and setting up temperature monitoring points; laying a cooling water pipe network in the concrete pouring area of ​​the tunnel; setting up temperature sensors to monitor the core temperature and surface temperature of the concrete; establishing a temperature data acquisition system to record the temperature change curve during the pouring process in real time; calculating the required cooling water flow rate through the hydration heat temperature rise control equation; performing pre-deformation compensation design for the formwork; using a 3D laser scanner to comprehensively scan the installed formwork to obtain initial surface coordinate data; establishing a formwork deformation prediction equation based on the concrete lateral pressure distribution law and formwork stiffness parameters; and calculating the deformation during the pouring process. The expected deformation of each part of the formwork is compensated by reversing the allowance at each measuring point during formwork installation; the concrete is poured in layers and the temperature rise rate is dynamically controlled, using a fast insertion and slow withdrawal vibration process with an attached vibrator to remove air bubbles, and the cooling water flow rate is adjusted in real time based on temperature monitoring data and the hydration heat temperature rise control equation; early curing is implemented and shrinkage deformation is monitored, and spray curing is started immediately after the concrete has set to keep the surface moist, and the surface is covered with a moisturizing curing material for continuous curing. During the curing period, strain gauges are used to measure the shrinkage strain of the concrete surface, and the adjustment amount of spraying frequency and covering material thickness is calculated through a shrinkage strain control game model; after demolding, precise measurement and deviation correction are carried out, using a three-dimensional laser scanner and a total station to measure the actual surface coordinates of the inner surface of the cavity, comparing and analyzing the measurement data with the design surface coordinates to establish a deviation distribution map, and feeding the deviation data back to the formwork deformation prediction equation for parameter correction.

[0005] Specifically, the dual-blending system is formed by mixing fly ash at a dosage of 30 to 40% and mineral powder at a dosage of 20 to 30%.

[0006] Specifically, the basalt fibers are 12 to 18 mm in length and 13 to 17 mm in diameter. Inorganic fibers, with a doping amount of 3 to 5 .

[0007] The dual-expansion source expansion agent is composed of calcium oxide expansion component and magnesium oxide expansion component, with a dosage of 8 to 12%.

[0008] Specifically, the "dry mixing followed by wet mixing" means first dry mixing for 30 seconds and then wet mixing for 90 seconds, with the mixing speed controlled at 35 to 45 revolutions per minute.

[0009] The cooling water pipe network is pre-embedded in the tunnel concrete in a quincunx pattern with a horizontal spacing of 600 to 800 mm and a vertical spacing of 500 to 700 mm.

[0010] The temperature sensors are arranged in groups every 1500 to 2000 mm, with the core temperature sensor embedded in the center of the concrete section and the surface temperature sensor embedded at a depth of 50 to 80 mm from the surface.

[0011] Specifically, the hydration heat temperature rise control equation is that the cooling water flow rate adjustment coefficient is equal to the ratio of the baseline adjustment coefficient multiplied by the ratio of the core temperature rise rate to the standard temperature rise rate to the power of 0.6, and the ratio of the core surface temperature difference to the standard temperature difference to the power of 0.4.

[0012] Specifically, the template deformation prediction equation is: the expected deformation at the template measuring point is equal to the 1.5th power of the ratio of the deformation benchmark value multiplied by the pouring height divided by the standard pouring height multiplied by the 0.5th power of the ratio of the pouring speed divided by the standard pouring speed.

[0013] Specifically, the layered concrete pouring involves pouring concrete in layers with a thickness of 300 to 500 mm, with the pouring speed controlled at 2 to 3 meters per hour.

[0014] Specifically, the fast insertion and slow withdrawal vibration process involves inserting a vibrator into the concrete at a speed of 50 to 70 mm per second, holding it for 10 to 15 seconds, and then slowly withdrawing it at a speed of 20 to 30 mm per second.

[0015] Specifically, the spray curing involves covering the concrete with a moisturizing curing material 6 hours after the concrete has set and continuing curing for more than 14 days. During the curing period, strain gauges are used to measure the shrinkage strain of the concrete surface every 8 hours.

[0016] The shrinkage strain control game model includes an upper-level model that aims to maximize the uniformity of moisture on the concrete surface and a lower-level model that aims to minimize the internal temperature gradient of the concrete.

[0017] Specifically, the objective function of the upper-level model is the square root of the ratio of the baseline uniformity index minus the ratio of the cumulative strain value to the critical strain value multiplied by the ratio of the spray frequency adjustment amount to the standard spray frequency.

[0018] Specifically, the objective function of the lower-level model is the square root of the ratio of the baseline gradient index minus the ratio of the core surface temperature difference to the critical temperature difference, multiplied by the ratio of the covering material thickness adjustment amount to the standard covering thickness.

[0019] Specifically, the template deformation prediction equation parameter correction involves using measured deviation data as feedback input and employing the least squares method to fit and correct the template stiffness parameters and boundary constraint conditions. The corrected template deformation prediction equation is then used to accurately calculate the template pre-deformation compensation amount during subsequent tunnel section construction.

[0020] The mineral powder is finely ground blast furnace slag, which has potential hydraulic properties.

[0021] This invention reduces the total heat of hydration to 60-70% of that of ordinary cement by formulating low-heat, high-crack-resistant concrete. A cooling water pipe network with a spacing of 600-800 mm is laid in the pouring area. The cooling water flow rate is dynamically adjusted based on real-time temperature monitoring data and the hydration heat temperature rise control equation, keeping the core temperature rise rate below 1.8℃ per hour. When the temperature difference between the core and the surface approaches 18℃, the system automatically activates an early warning and reduces the pouring speed. Simultaneously, a shrinkage strain control game model optimizes the frequency of spray curing and the thickness of the covering material, ensuring that the early accumulated strain of the concrete remains below the critical value, effectively avoiding the phenomenon of excessive tensile stress caused by excessive temperature gradients. In summary, this invention solves the technical problem mentioned in the background art of early cracking in cast-in-place fair-faced concrete wind tunnels caused by excessive internal and external temperature differences due to the heat of hydration temperature rise. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method of the present invention.

[0023] Figure 2 This is a diagram showing the expected deformation distribution of each measuring point in the template in the embodiment.

[0024] Figure 3 This is a scatter plot showing the distribution of surface deviations within the cavity in the embodiment.

[0025] Figure 4 This is a comparison chart of the prediction accuracy before and after the template stiffness parameter correction in the embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below.

[0027] like Figure 1 As shown, the present invention provides a method for crack-resistant construction of cast-in-place fair-faced concrete wind tunnel bodies, comprising:

[0028] S10. To prepare low-heat, high-crack-resistant concrete, fly ash is mixed with mineral powder at a dosage of 30-40% to form a dual-admixture system. Basalt fiber is added at a dosage of 3-5 kg ​​per cubic meter and dual-expansion source expansion agent at a dosage of 8-12%. Basalt fiber is pre-dispersed using an airflow dispersion device before being added to the mixture. The mixture is first dry-mixed for 30 seconds and then wet-mixed for 90 seconds. The mixing speed is controlled at 35-45 revolutions per minute.

[0029] S20. Pre-embed cooling water pipes and set up temperature monitoring points. Lay a cooling water pipe network with a spacing of 600-800mm in the concrete pouring area of ​​the tunnel. Set up a set of temperature sensors every 1500-2000mm to monitor the core temperature and surface temperature of the concrete respectively. Establish a temperature data acquisition system to record the temperature change curve in real time during the pouring process. Calculate the required cooling water flow rate through the hydration heat temperature rise control equation.

[0030] S30. Conduct pre-deformation compensation design for the template. Use a 3D laser scanner to scan the installed template to obtain the initial surface coordinate data. Based on the distribution law of concrete lateral pressure and template stiffness parameters, establish the template deformation prediction equation, calculate the expected deformation of each part of the template during the pouring process, and make reverse pre-compensation for each measuring point position during template installation.

[0031] S40. Pour concrete in layers and dynamically control the temperature rise rate. Pour in layers with a thickness of 300-500mm each. Use a fast insertion and slow withdrawal vibration process with an attached vibrator to remove air bubbles. Control the pouring speed at 2-3 meters per hour. Adjust the cooling water flow rate in real time based on the temperature monitoring data and the calculation results of the hydration heat temperature rise control equation in step S20. Increase the water flow rate when the core temperature rise rate exceeds 1.8℃ per hour. When the temperature difference between the core and the surface is close to 18℃, activate the warning and reduce the pouring speed.

[0032] S50. Implement early curing and monitor shrinkage deformation. Immediately after the concrete has set, start spray curing to keep the surface moist. After 6 hours, cover with a moisturizing curing material and continue curing for more than 14 days. During the curing period, use strain gauges to measure the shrinkage strain of the concrete surface every 8 hours. Calculate the adjustment amount of spraying frequency and covering material thickness through a shrinkage strain control game model. When the cumulative strain exceeds 200 microstrains, execute the adjustment scheme output by the shrinkage strain control game model. When the temperature difference between the core and the surface drops below 15°C, gradually reduce the cooling water circulation intensity.

[0033] S60. After demolding, precise measurement and deviation correction are performed. A three-dimensional laser scanner and a total station are used to measure the actual surface coordinates of the inner surface of the tunnel. The measurement data is compared and analyzed with the design surface coordinates to establish a deviation distribution map. The location and deviation pattern of the area with a deviation exceeding ±1.5mm are identified. The deviation data is fed back to the template deformation prediction equation in step S30 for parameter correction, which is used to guide the template pre-deformation compensation design of subsequent tunnel sections.

[0034] The dual-admixture system is used to reduce the rate and total amount of heat release from concrete hydration. Fly ash, consisting of spherical glassy particles with pozzolanic activity, participates in the reaction during the later stages of hydration. Mineral powder, finely ground blast furnace slag, possesses potential hydraulic properties. The synergistic effect of both delays the appearance of the peak heat release during hydration and lowers the peak temperature, reducing the total heat of hydration to 60-70% of that of ordinary Portland cement. The ratio of fly ash (30-40%) to mineral powder (20-30%) is determined through concrete hydration heat experiments. The experimental method involves preparing different admixtures... Nine groups of concrete test blocks were prepared for each batch, with three test blocks in each group measuring 150mm×150mm×150mm. The temperature rise curves were continuously monitored for 72 hours in a constant temperature environment of 20℃ using a semi-adiabatic temperature rise tester. The highest temperature rise value and the time to reach the highest temperature rise of each group of test blocks were recorded. The influence weights of fly ash content and mineral powder content on the peak temperature of hydration heat were determined by orthogonal experimental analysis. The mix proportion with a peak temperature below 55℃ and a peak time delayed by more than 24 hours was selected as the optimal mix proportion range of the dual-admixture system.

[0035] The basalt fibers are inorganic fibers with a length of 12–18 mm and a diameter of 13–17 micrometers, possessing a tensile strength of 3000–4800 MPa and an elastic modulus of 93–110 GPa. They form a three-dimensional randomly distributed reinforcing network in the concrete matrix, preventing the propagation of microcracks through frictional interlocking at the fiber-matrix interface, thereby increasing the tensile strain capacity of the concrete to 180–220 microstrains. The dosage of basalt fibers (3–5 kg / m³) was determined through concrete crack resistance tests. The test method involved preparing fiber dosages of 0 kg / m³, 1 kg / m³, 2 kg / m³, and 3 kg / m³, respectively. Six groups of concrete slab specimens with fiber content of 4 kg / m³, 5 kg / m³, and 6 kg / m³ were prepared, each measuring 600 mm × 600 mm × 100 mm. After curing under standard conditions for 28 days, a constrained shrinkage cracking test was conducted. The cracking time and crack width of each group of specimens were measured using the steel ring constraint method. At the same time, the tensile strain value of each group of concrete was measured. The relationship curve between fiber content and crack resistance was obtained by analyzing the experimental data. The fiber content range with a tensile strain of 180 microstrain or higher and a crack width of less than 0.05 mm was selected as the optimal content of the basalt fiber.

[0036] The dual-expansion-source expansive agent is composed of calcium oxide and magnesium oxide expansive components. Calcium oxide hydrates to form calcium hydroxide, generating early expansion that compensates for concrete plastic shrinkage within 1-3 days. Magnesium oxide hydrates to form magnesium hydroxide, generating later expansion that compensates for concrete autogenous shrinkage and drying shrinkage within 7-28 days. The complementary action of these two components achieves a full-age shrinkage compensation effect. The dual-expansion-source expansive agent is formulated with calcium oxide comprising 60-70% of the total expansive agent and magnesium oxide comprising 30-40%. The dosage of the dual-expansion-source expansive agent is 8-12%. The optimal dosage of the dual-expansion-source expansive agent was determined through concrete expansion rate experiments. The experimental method involved preparing six groups of concrete prism specimens with expansion agent dosages of 0%, 4%, 6%, 8%, 10%, 12%, and 14%, each group measuring 100mm × 100mm × 515mm. Under standard curing conditions, the expansion rate was continuously measured for 56 days, and the shrinkage rate at the same age was also measured. The shrinkage compensation rate under each dosage was calculated based on the experimental data. The optimal dosage of the dual-expansion-source expansive agent was selected from the range where the shrinkage compensation rate reached 85-95% and the expansion rate did not exceed 0.08%.

[0037] The airflow dispersion device disperses basalt fiber bundles into monofilaments using high-speed rotating airflow, preventing the fibers from re-agglomerating during concrete mixing. Adding 0.3-0.5% dispersant before feeding improves fiber surface wettability, resulting in a fiber dispersion uniformity coefficient of over 0.85 in the matrix. This dispersion uniformity coefficient is calculated using image analysis to determine the coefficient of variation of fiber distribution in the concrete cross-section. The calculation method involves cutting a 100mm × 100mm cross-section from a concrete sample, capturing the cross-section image with a high-resolution camera, performing grayscale processing, dividing the cross-section into 25 20mm × 20mm sub-regions, counting the number of fibers in each sub-region, and calculating the ratio of the standard deviation to the average of the fiber counts in the 25 sub-regions to obtain the coefficient of variation. The dispersion uniformity coefficient is 1 minus the coefficient of variation.

[0038] In the aforementioned dry-mixing followed by wet-mixing process, the dry-mixing stage involves thoroughly mixing cement, fly ash, mineral powder, and sand and gravel aggregates to form a uniform dry material. In the wet-mixing stage, water and admixtures are added, and stirring continues to disperse the basalt fibers in the flowing slurry. The stirring speed is 35-45 revolutions per minute to ensure fiber dispersion while avoiding excessive speed that could cause fiber breakage. The total stirring time is controlled at 120 seconds to prevent fiber damage due to over-mixing. The time allocation of 30 seconds of dry mixing followed by 90 seconds of wet mixing was determined through fiber dispersion effect experiments. The experimental method involved setting dry mixing times of 10 seconds, 20 seconds, and 30 seconds respectively. The wet mixing times were 110 seconds, 100 seconds, 90 seconds, 80 seconds, and 70 seconds, respectively, with a total mixing time of 120 seconds. Three sets of concrete test blocks were prepared for each time combination. The dispersion uniformity coefficient and fiber integrity rate of each set were measured. The fiber integrity rate is the percentage of fibers with a length greater than 10 mm in the cross-sectional image out of the total number of fibers. Through experimental data analysis, the time allocation with a dispersion uniformity coefficient greater than 0.85 and a fiber integrity rate greater than 90% was selected as the optimal parameters for the dry-mix-then-wet-mix process.

[0039] The cooling water pipe network uses high-density polyethylene pipes with a diameter of 25-32mm, pre-embedded in the tunnel concrete in a staggered arrangement with a horizontal spacing of 600-800mm and a vertical spacing of 500-700mm. The cooling water inlet temperature is controlled at 15-20℃, and the flow rate is dynamically adjusted within a range of 8-15 cubic meters per hour based on temperature monitoring data and the hydration heat temperature rise control equation. The circulation cooling duration is 5-7 days after the concrete pouring is completed. The arrangement parameters of the horizontal spacing of 600-800mm and the vertical spacing of 500-700mm are determined through numerical simulation experiments of the concrete temperature field. The experimental method is to establish a three-dimensional finite element model of the tunnel concrete, set different combinations of cooling water pipe spacing to conduct transient temperature field analysis, simulate the temperature distribution within 168 hours after pouring, calculate the maximum core temperature and maximum temperature gradient under each combination, and select the spacing combination with a maximum core temperature below 70℃ and a maximum temperature gradient of less than 15℃ per meter as the arrangement parameters of the cooling water pipe network.

[0040] The temperature sensor uses a platinum resistance temperature sensor with an accuracy of ±0.2℃. The core temperature sensor is embedded in the center of the concrete section, and the surface temperature sensor is embedded at a depth of 50-80mm from the surface. The temperature data acquisition interval is 15 minutes, and the data is transmitted to the monitoring terminal in real time via wireless transmission to form a temperature history curve. The core temperature and surface temperature are used as input parameters for the hydration heat temperature rise control equation to calculate the required cooling water flow rate.

[0041] The hydration heat temperature rise control equation is used to calculate the cooling water flow rate adjustment coefficient based on the concrete core temperature rise rate and the core surface temperature difference. The inputs include the concrete core temperature rise rate in °C per hour and the core surface temperature difference in °C. The output is the cooling water flow rate adjustment coefficient, a dimensionless value. The hydration heat temperature rise control equation is expressed as follows: The cooling water flow rate adjustment coefficient equals the base adjustment coefficient multiplied by the ratio of the core temperature rise rate to the standard temperature rise rate (0.6 power) multiplied by the ratio of the core surface temperature difference to the standard temperature difference (0.4 power), where the base adjustment coefficient is 1.0, the standard temperature rise rate is 2.0 °C per hour, and the standard temperature difference is 2... At 0℃, the calculated cooling water flow rate adjustment coefficient is multiplied by the benchmark cooling water flow rate of 10 cubic meters per hour to obtain the actual required cooling water flow rate. The benchmark adjustment coefficient, standard temperature rise rate, and standard temperature difference are calibrated through concrete temperature control experiments. The experimental method involves preparing concrete with the same mix proportion as the construction mix and pouring it into a large-volume test block of 1500mm×1500mm×1500mm. Cooling water pipes and temperature sensors are pre-embedded, and the temperature change process is monitored under different cooling water flow rates. The correspondence between the core temperature rise rate and temperature difference and the cooling water flow rate is recorded. The exponential coefficient and standard parameter value in the hydration heat temperature rise control equation are determined through multivariate nonlinear regression analysis.

[0042] The template deformation prediction equation is used to calculate the expected deformation of each measuring point of the template based on the concrete pouring height and pouring speed. The inputs include the concrete pouring height in meters and the pouring speed in meters per hour. The output is the expected deformation of the template measuring point in mm. The template deformation prediction equation is expressed as follows: The expected deformation of the template measuring point is equal to the deformation reference value multiplied by the ratio of the pouring height to the standard pouring height, multiplied by the ratio of the pouring speed to the standard pouring speed, and then multiplied by the ratio of the pouring speed to the standard pouring speed. The deformation reference value is 3.0 mm, the standard pouring height is 5.0 meters, and the standard... The pouring speed is 2.5 meters per hour. The deformation benchmark value, standard pouring height, and standard pouring speed are calibrated through template deformation experiments. The experimental method involves building a template system identical to that used in actual construction, pouring concrete under different pouring heights and speeds, using displacement sensors to monitor the template deformation in real time, recording the maximum deformation value and its distribution under each working condition, and determining the exponential coefficient and standard parameter value in the template deformation prediction equation through multivariate nonlinear regression analysis. The expected deformation of the template measuring points is used to determine the value of the reverse reserved compensation in step S30.

[0043] The three-dimensional laser scanner has a scanning accuracy of ±1mm and a point cloud density of 2-3mm. The scanning process adopts a multi-site scanning method to ensure that there are no blind spots on the inner surface of the cave. The point cloud data obtained by scanning is processed by coordinate transformation and stitching to form a complete three-dimensional model. After comparing the best fit with the design model, the normal deviation value of each measuring point is extracted. The initial surface coordinate data is used as the reference input for the template deformation prediction equation to calculate the theoretical coordinates of each measuring point position.

[0044] The fast-insertion, slow-withdrawal vibration process involves inserting a vibrator into the concrete at a speed of 50–70 mm / s to a depth of 50–100 mm in the lower layer of concrete, holding it for 10–15 seconds, and then slowly withdrawing it at a speed of 20–30 mm / s. The spacing between insertion points is controlled within 1.5 times the radius of action of the vibrator. The attached vibrator is fixed to the outside of the formwork and vibrates at a frequency of 40–50 Hz to promote concrete compaction and air release. The parameters of the insertion speed of 50–70 mm / s and the withdrawal speed of 20–30 mm / s in the fast-insertion, slow-withdrawal vibration process are determined by a concrete air bubble rate experiment. The experimental method involves pouring concrete in a transparent formwork and vibrating it using different combinations of vibrator insertion and withdrawal speeds. Immediately after vibration, a photograph of the formwork surface is taken, and the number of air bubbles with a diameter greater than 2 mm and the percentage of air bubble area are counted using image processing software. The vibration parameter combination with a percentage of air bubble area less than 0.5% is selected as the optimal operating parameters for the fast-insertion, slow-withdrawal vibration process.

[0045] The strain gauge is a resistance strain gauge with a gauge length of 100 mm and a sensitivity coefficient of 2.0 ± 0.01. It is pasted on the concrete surface at the position where it is in direct contact with the curing material. Strain data is collected every 8 hours to calculate the cumulative strain value. When the cumulative strain exceeds 200 microstrains, the adjustment scheme output by the shrinkage strain control game model is executed. The cumulative strain value is used as the input parameter of the shrinkage strain control game model to calculate the adjustment amount of spraying frequency and the adjustment amount of covering material thickness.

[0046] The shrinkage strain control game model includes an upper-level model aiming to maximize the uniformity of moisture distribution on the concrete surface and a lower-level model aiming to minimize the internal temperature gradient of the concrete. The objective function of the upper-level model is used to optimize the spray frequency adjustment to improve the uniformity of moisture distribution on the concrete surface. The inputs include the cumulative strain value in microstrain and the current spray frequency in times per hour. The output is the spray frequency adjustment in times per hour. The objective function of the upper-level model is expressed as follows: the surface moisture uniformity index is equal to the square root of the ratio of the cumulative strain value divided by the critical strain value to the baseline uniformity index, multiplied by the ratio of the spray frequency adjustment to the standard spray frequency. The baseline uniformity index is 0.95, and the critical strain value is 250 microstrains. The standard spray frequency is 0.5 times per hour. The upper-level model constraint is that the spray frequency adjustment is greater than or equal to 0 times per hour and less than or equal to 0.5 times per hour. The lower-level model's objective function is used to optimize the adjustment of the covering material thickness to reduce the temperature gradient amplitude inside the concrete. The inputs include the core-to-surface temperature difference in °C and the current covering material thickness in mm. The output is the covering material thickness adjustment in mm. The lower-level model's objective function is expressed as follows: the temperature gradient control index equals the square root of the ratio of the core-to-surface temperature difference divided by the critical temperature difference, multiplied by the ratio of the covering material thickness adjustment divided by the standard covering thickness, where the baseline gradient index is 0.90 and the critical temperature difference is 20. The standard cover thickness is 10mm, and the lower model constraint is that the cover material thickness adjustment is greater than or equal to 0mm and less than or equal to 20mm. The coupling term between the objective function of the upper model and the objective function of the lower model is the product of the cumulative strain value and the temperature difference between the core and the surface, divided by the coupling standard value, which is 5000 microstrain·℃. The coupling term reflects the mutual influence between surface shrinkage strain and internal temperature gradient. By solving the shrinkage strain control game model, the optimal spray frequency adjustment and cover material thickness adjustment are obtained to simultaneously achieve the optimal surface humidity uniformity index and temperature gradient control index. The baseline uniformity index, critical strain value, standard spray frequency, and baseline in the shrinkage strain control game model are defined. The gradient index, critical temperature difference, standard cover thickness, and coupling standard value were calibrated through concrete curing effect experiments. The experimental method involved preparing 18 groups of 1000mm×1000mm×200mm concrete slab specimens, each cured with different combinations of spraying frequency and cover material thickness. During the curing process, the surface humidity distribution and internal temperature gradient were continuously monitored. After 14 days of curing, the surface cracks of each group of specimens were examined. The influence of spraying frequency and cover material thickness on surface humidity uniformity and temperature gradient was determined by orthogonal experimental analysis. The mathematical relationship between cumulative strain, temperature difference, and curing parameter adjustment was established. The parameter values ​​in the shrinkage strain control game model were obtained by solving the problem using a multi-objective optimization algorithm.

[0047] The total station has a measurement accuracy of ±1mm. A network of control points is set up inside the tunnel as a measurement benchmark. The total station is used to supplement the measurement of key parts that cannot be accurately measured by the laser scanner. The two sets of measurement data are fused to form high-precision profile data. The deviation distribution map displays the spatial distribution characteristics of different deviation intervals in the form of a chromatogram. The deviation data includes the normal deviation value between the actual profile coordinates and the design profile coordinates of each measuring point.

[0048] The template deformation prediction equation parameter correction process uses measured deviation data as feedback input, employs the least squares method to fit and correct the template stiffness parameters and boundary constraints, and reduces the error between the predicted deformation and the measured deformation to within ±0.3 mm through iterative calculation. The corrected template deformation prediction equation is used for the accurate calculation of template pre-deformation compensation during subsequent tunnel section construction, realizing closed-loop control and continuous optimization based on measured data. The template stiffness parameter correction is calculated by dividing the difference between the measured deviation value and the predicted deviation value by the template stiffness sensitivity coefficient. The template stiffness sensitivity coefficient is the partial derivative of the template deformation with respect to the template stiffness parameter, and its value range is 0.8–1.2 mm per gigapascal calculated by the finite element analysis method. The boundary constraint correction is determined by the difference between the measured support point displacement and the predicted support point displacement. The corrected template stiffness parameters and boundary constraints are substituted into the template deformation prediction equation to recalculate the expected deformation and reverse reserved compensation of each measuring point in the next tunnel section.

[0049] The specific implementation methods of the above steps are described in detail below.

[0050] The specific implementation of step S10 is to prepare low-heat, high-crack-resistant concrete. First, fly ash at a dosage of 30-40% and mineral powder at a dosage of 20-30% are compounded to form a dual-admixture system to reduce the hydration heat release rate. Fly ash, being spherical glassy particles with pozzolanic activity, participates in the reaction in the later stages of hydration. Mineral powder, being finely ground blast furnace slag, has potential hydraulic properties. The two work synergistically to delay the appearance time of the hydration heat release peak and reduce the peak temperature, reducing the total hydration heat to 60-70% of that of ordinary Portland cement. Then, 3-5 kg / kg of basalt fiber is added. The basalt fibers, containing 8-12% dual-expansion-source expansive agent, have a length of 12-18 mm, a diameter of 13-17 μm, a tensile strength of 3000-4800 MPa, and an elastic modulus of 93-110 GPa. They form a three-dimensional randomly distributed reinforcing network in the concrete matrix, preventing microcrack propagation through interfacial friction between the fibers and the matrix, thus increasing the tensile strain capacity of the concrete to 180-220 microstrain. The dual-expansion-source expansive agent is a compound of 60-70% calcium oxide and 30-40% magnesium oxide. Calcium oxide hydrates to form calcium hydroxide, resulting in early expansion compensating for plastic shrinkage within 1-3 days. Magnesium oxide hydrates to form magnesium hydroxide, resulting in later expansion compensating for auto-shrinkage and drying shrinkage within 7-28 days, achieving full-age shrinkage compensation. An airflow dispersion device is used to pre-disperse the basalt fibers. High-speed rotating airflow disperses the fiber bundles into monofilaments to prevent re-agglomeration during mixing. Before feeding, 0.3-0.5% dispersant is added to improve the wettability of the fiber surface, ensuring a fiber dispersion uniformity coefficient of over 0.85. After feeding, dry mixing is performed for 30 seconds to fully mix cement, fly ash, mineral powder, and sand and gravel aggregates to form a uniform dry material. Then, wet mixing is performed for 90 seconds, adding water and admixtures to ensure uniform dispersion of basalt fibers in the flowing slurry. The mixing speed is controlled at 35-45 r / min to ensure full fiber dispersion while avoiding excessive speed that could cause fiber breakage. The total mixing time is 120 seconds to prevent fiber damage due to over-mixing. The purpose of this step is to reduce the total heat of hydration through a dual-admixture system, enhance the crack resistance of concrete through basalt fibers, and compensate for shrinkage deformation throughout the entire lifespan through a dual-expansion source expansion agent, thereby improving the crack resistance of concrete from a material perspective.

[0051] The specific implementation of step S20 involves pre-embedding cooling water pipes and setting up temperature monitoring points. High-density polyethylene pipes with a diameter of 25-32mm are used, pre-embedded in the concrete of the tunnel in a staggered pattern with a horizontal spacing of 600-800mm and a vertical spacing of 500-700mm. This staggered arrangement ensures uniform spatial distribution of the cooling water pipes, improving cooling efficiency and avoiding localized temperature concentration. The cooling water inlet temperature is controlled at 15-20℃. A set of temperature sensors is installed every 1500-2000mm, using platinum resistance temperature sensors with an accuracy of ±0.2℃. The core temperature sensor is embedded at the center of the concrete cross-section to monitor the highest internal temperature of the concrete, while the surface temperature sensor is embedded at a depth of 50-80mm from the surface to monitor surface temperature changes, establishing a temperature data system. According to the acquisition system, temperature data is collected every 15 minutes. The data is transmitted wirelessly to the monitoring terminal in real time to form a temperature history curve. The required cooling water flow rate is calculated using the hydration heat temperature rise control equation. This equation uses the concrete core temperature rise rate and the core-to-surface temperature difference as input parameters and outputs a cooling water flow rate adjustment coefficient. This coefficient is calculated by multiplying a baseline adjustment coefficient by 0.6 times the ratio of the core temperature rise rate to the standard temperature rise rate, and then by 0.4 times the ratio of the core-to-surface temperature difference to the standard temperature difference. The baseline adjustment coefficient is 1.0, the standard temperature rise rate is 2.0℃ / h, and the standard temperature difference is 20℃. The calculated cooling water flow rate adjustment coefficient is then multiplied by the baseline cooling water flow rate. The actual required cooling water flow rate is obtained by using the equation in power function form to reflect the nonlinear influence of the temperature rise rate and temperature difference on the cooling demand. The exponents 0.6 and 0.4 reflect the relative weights of the two factors. The purpose of this step is to actively control the temperature rise of the concrete hydration heat through the cooling water pipe network, and to prevent temperature cracks caused by excessive temperature and temperature gradient through real-time temperature monitoring and dynamic flow regulation.

[0052] The specific implementation of step S30 involves designing a pre-deformation compensation for the template. A 3D laser scanner is used to perform a comprehensive scan of the installed template, with a scanning accuracy of ±1mm and a point cloud density of 2-3mm. The scanning process employs a multi-site scanning method to ensure no blind spots on the inner surface of the cavity. The obtained point cloud data is transformed and stitched together to form a complete 3D model. Initial surface coordinate data is extracted as a reference. Based on the concrete lateral pressure distribution and template stiffness parameters, a template deformation prediction equation is established. This equation uses the concrete pouring height and pouring speed as input parameters and outputs the expected deformation at the template measuring points. The expected deformation at the template measuring points is calculated by multiplying the deformation reference value by the 1.5th power of the ratio of the pouring height to the standard pouring height, and then multiplying it by the 0.5th power of the ratio of the pouring speed to the standard pouring speed. The deformation reference value is 3.0 mm, the standard pouring height is 5.0 m, and the standard pouring speed is 2.5 m / h. The equation uses a power function to reflect the dominant role of pouring height on formwork deformation and the secondary influence of pouring speed. An exponent of 1.5 indicates that the formwork deformation increases non-linearly with the pouring height, and an exponent of 0.5 indicates that the influence of pouring speed on deformation is relatively weak. After calculating the expected deformation of each part of the formwork, a reverse pre-compensation amount is reserved at each measuring point during formwork installation. That is, the measuring point is pre-offset inward by a distance equivalent to the expected deformation, so that the formwork deforms to the designed surface position under the action of concrete lateral pressure after pouring. The purpose of this step is to eliminate the influence of formwork deformation on the surface accuracy of the cavity by predicting the deformation of the formwork during the pouring process and pre-compensating for it, and to ensure that the inner surface of the cavity meets the design requirements after demolding.

[0053] The specific implementation of step S40 involves layered concrete pouring with dynamic temperature rise rate control. Each layer is 300-500mm thick. Layered pouring reduces the height of each pour, lowers the lateral pressure on the concrete, and facilitates compaction and temperature control. The pouring speed is controlled at 2-3 m / h, employing a quick-insertion, slow-withdrawal vibration process. The vibrator is inserted into the concrete at a speed of 50-70 mm / s to a depth of 50-100 mm in the next layer. Rapid insertion allows the vibrator to quickly reach the predetermined position, minimizing disturbance to the already poured concrete. A 10-15 second pause allows for full transmission of vibration waves, promoting concrete compaction and air bubble uplift. Then, the vibrator is slowly withdrawn at a speed of 20-30 mm / s. Slow withdrawal prevents voids from forming along the vibrator's path. The spacing between insertion points is controlled within 1.5 times the vibrator's radius of action to ensure proper compaction. Without omission, an attached vibrator is fixed to the outside of the formwork and vibrates at a frequency of 40-50Hz to promote concrete compaction and help remove air bubbles close to the formwork surface. The cooling water flow rate is adjusted in real time based on the temperature monitoring data and the calculation results of the hydration heat temperature rise control equation in step S20. When the core temperature rise rate exceeds 1.8℃ / h, the water flow rate is increased to enhance the cooling intensity. When the temperature difference between the core and the surface is close to 18℃, an early warning is activated and the pouring speed is reduced. Slowing down the concrete pouring speed can reduce the total amount of hydration heat released per unit time, giving the cooling system more time to dissipate heat and preventing excessive temperature gradient from causing surface cracks. The purpose of this step is to ensure the compactness and appearance quality of concrete through reasonable pouring technology and vibration method, and to control the temperature rise rate and temperature gradient within a safe range through real-time temperature monitoring and dynamic flow rate adjustment.

[0054] The specific implementation of step S50 involves early curing and monitoring shrinkage deformation. Immediately after the concrete has set, spray curing begins to maintain surface moisture and prevent rapid evaporation of surface moisture, which can cause plastic shrinkage cracks. After 6 hours, a moisturizing curing material is applied and curing continues for at least 14 days. The moisturizing curing material can be geotextile or a curing blanket. Covering creates a moist microenvironment to slow moisture loss. During the curing period, resistance strain gauges are used every 8 hours to measure the shrinkage strain on the concrete surface. The strain gauges have a gauge length of 100 mm and a sensitivity coefficient of 2.0 ± 0.01. They are attached to the concrete surface at the point of direct contact with the curing material, and the cumulative strain value is calculated. When the cumulative strain exceeds 200 microstrains, the adjustment scheme output by the shrinkage strain control game model is executed. The shrinkage strain control game model includes an upper-level model and a lower-level model. The upper-level model optimizes the spraying frequency adjustment with the goal of maximizing the uniformity of concrete surface humidity. The input is the cumulative strain value and the current spraying frequency. Next, the output spray frequency adjustment amount is calculated. The lower-level model optimizes the covering material thickness adjustment amount with the goal of minimizing the internal temperature gradient of the concrete. The input is the core-to-surface temperature difference and the current covering material thickness, and the output is the covering material thickness adjustment amount. The two models are linked by a coupling term, which is the product of the cumulative strain value and the core-to-surface temperature difference divided by the coupling standard value of 5000 microstrain·℃. This reflects the mutual influence between surface shrinkage strain and internal temperature gradient. By solving this two-layer optimization model, the curing parameter adjustment scheme that simultaneously achieves optimal surface humidity uniformity and temperature gradient control is obtained. When the core temperature and surface temperature difference drop below 15℃, the cooling water circulation intensity is gradually reduced to avoid excessive cooling that leads to an excessive temperature difference between the inside and outside of the concrete. The purpose of this step is to control the surface shrinkage strain and internal temperature gradient of the concrete within a reasonable range through a scientific curing system and real-time monitoring and control, so as to prevent the generation of early shrinkage cracks and temperature cracks.

[0055] The specific implementation of step S60 involves precise measurement and deviation correction after demolding. A 3D laser scanner and a total station are used to jointly measure the actual surface coordinates of the tunnel interior. The 3D laser scanner is used for comprehensive scanning to acquire overall surface data, while the total station has a measurement accuracy of ±1mm. A control point network is set up inside the tunnel as a measurement benchmark. Supplementary measurements are performed on key areas that the laser scanner cannot accurately measure. The two sets of measurement data are fused to form high-precision surface data. The measurement data is compared and analyzed with the designed surface coordinates. The normal deviation value of each measuring point is extracted through best-fit comparison, and a deviation distribution map is established to display the spatial distribution characteristics of different deviation intervals in a chromatogram format. The location and deviation pattern of areas with deviations exceeding ±1.5mm are identified. The deviation data is fed back to the template deformation prediction equation in step S30 for parameter correction, using the least squares method for fitting and correction. The template stiffness parameters and boundary constraints are determined by dividing the difference between the measured and predicted deviation values ​​by the template stiffness sensitivity coefficient, which is the partial derivative of the template deformation with respect to the template stiffness parameter and ranges from 0.8 to 1.2 mm / GPa. The boundary constraint correction is determined by the difference between the measured and predicted support point displacements. Iterative calculations reduce the error between the predicted and measured deformation to within ±0.3 mm. The corrected template stiffness parameters and boundary constraints are then substituted into the template deformation prediction equation to recalculate the expected deformation and reverse compensation for each measuring point in the next tunnel section. The purpose of this step is to correct the template deformation prediction equation parameters through measured data feedback, achieve closed-loop control and continuous optimization based on measured data, and improve the surface accuracy control level of subsequent tunnel sections.

[0056] It should be noted that the key technical concepts of this invention include the design of a dual-admixture system and a dual-expansion-source composite formulation, a cooling water pipe network and a dynamic temperature control system, template pre-deformation compensation and measured feedback closed-loop control, and shrinkage strain monitoring and game-theoretic model-optimized curing. The dual-admixture system and dual-expansion-source composite formulation, through the synergistic effect of fly ash and mineral powder, reduce the rate and total amount of hydration heat release, delaying the time of peak heat release. Combined with a basalt fiber three-dimensional reinforcement network to prevent microcrack propagation, and the dual-expansion-source expansion agent to achieve complementary compensation for early and late shrinkage, fundamentally improves the crack resistance and volume stability of concrete. Compared to single admixtures or single expansion sources, this composite system can comprehensively control the two major crack-inducing factors: hydration heat and shrinkage deformation. The cooling water pipe network and dynamic temperature control system achieves uniform spatial cooling through a quincunx arrangement of cooling water pipes. Based on real-time temperature monitoring data and the hydration heat temperature rise control equation, the cooling water flow rate is dynamically calculated and adjusted, ensuring that the temperature rise rate and temperature gradient are always under control. Compared to traditional fixed-flow cooling methods, this dynamic control system can accurately match the cooling intensity according to the actual temperature state of the concrete, avoiding over-cooling or under-cooling. The template pre-deformation compensation and measured feedback closed-loop control calculates the expected deformation during the pouring process by establishing a template deformation prediction equation and pre-compensates for it. After demolding, the measured deviation data is fed back to correct the prediction equation parameters, forming a closed-loop optimization cycle of measurement feedback correction and re-prediction. Compared with traditional experience-based adjustment methods, this closed-loop control system can gradually improve the deformation prediction accuracy, making the shape control of subsequent tunnel sections increasingly accurate. The synergistic effect of the above technical ideas is to systematically solve the problems of crack resistance and shape accuracy control of cast-in-place fair-faced concrete wind tunnels from multiple levels, including improving concrete material performance, actively controlling temperature during construction, accurately compensating for template deformation, and intelligently optimizing curing parameters. Compared with traditional methods that rely on single technical measures and cannot simultaneously achieve both crack resistance and shape accuracy, this invention achieves the organic unity of enhanced inherent crack resistance of materials and precise control of the construction process through multi-technology synergy, ensuring that the wind tunnel is both crack-free and meets strict shape accuracy requirements.

[0057] It should be noted that this invention also solves the following technical problem: the problem of formwork deformation during concrete pouring causing the surface accuracy deviation of the wind tunnel to exceed the design tolerance range. In wind tunnel construction, the surface accuracy of the inner surface of the tunnel must be within ±1.5 mm. Traditional construction methods position the formwork according to the design surface during installation, but the lateral pressure during concrete pouring causes elastic deformation of the formwork. After pouring, the concrete hardens and solidifies, and after demolding, the actual surface deviates systematically from the design surface. This invention uses a 3D laser scanner to obtain the initial surface coordinate data of the formwork, establishes a formwork deformation prediction equation based on the concrete lateral pressure distribution law and formwork stiffness parameters, calculates the expected deformation of each part during pouring, and makes reverse pre-compensation allowances at each measuring point during formwork installation, so that the surface of the formwork after deformation under the concrete lateral pressure exactly meets the design requirements. After demolding, the actual deviation data is obtained through precise measurement. The least squares method is used to correct the parameters of the template deformation prediction equation, so that the prediction accuracy is continuously improved. The surface accuracy deviation of the subsequent tunnel sections gradually converges to within ±0.3 mm, which meets the strict requirements of wind tunnel for surface accuracy.

[0058] This invention also solves the technical problem of unstable crack resistance caused by uneven dispersion of basalt fibers during concrete mixing. Although basalt fibers have excellent mechanical properties and crack-reinforcing effects, the fibers themselves exist in bundles, which easily lead to fiber agglomeration during concrete mixing. This results in uneven fiber distribution in the concrete matrix, with dense fibers in some areas and sparse fibers in others, causing fluctuations in the overall crack resistance of the concrete. This invention uses an airflow dispersion device to disperse the fiber bundles into monofilaments through high-speed rotating airflow. A dispersant is used to improve the wettability of the fiber surface. A two-stage mixing process—dry mixing followed by wet mixing—is employed. In the dry mixing stage, a uniform dry material base is formed. In the wet mixing stage, the fibers are fully dispersed in the flowing slurry. The mixing speed is controlled at 35 to 45 revolutions per minute to ensure fiber dispersion while preventing fiber breakage. This results in a fiber dispersion uniformity coefficient of over 0.85 and a fiber integrity rate of over 90% in the concrete matrix, ensuring the stability and reliability of the concrete's crack resistance.

[0059] Specifically, the principle of this invention is as follows: The technical problem solved by this invention lies in reducing the intensity of hydration heat release from the source and dynamically regulating the temperature field distribution through a multi-level collaborative control system. First, the pozzolanic activity and potential hydraulic properties of fly ash and mineral powder in the dual-blending system transform the hydration reaction from a concentrated burst to a slow, continuous release. The peak hydration heat time is delayed from 12 hours to over 24 hours, and the peak temperature is reduced from 65℃ to below 55℃, creating favorable conditions for subsequent temperature control. Second, the cooling water pipe network is arranged in a quincunx pattern inside the concrete, transferring core heat outwards through water circulation. The hydration heat temperature rise control equation calculates the optimal cooling water flow rate in real time based on the core temperature rise rate and temperature gradient, achieving a dynamic balance between the heat removal rate and the hydration heat release rate, thus avoiding uncontrolled rise in core temperature and a sharp increase in the temperature gradient. Furthermore, the three-dimensional reinforcing network formed by basalt fibers in the concrete matrix prevents microcrack propagation through interfacial frictional interlocking, increasing the tensile strain capacity of the concrete to 180-220 microstrain. The compensating expansion of the dual-expansion-source expansion agent at different ages offsets the tensile strain caused by plastic shrinkage and autogenous shrinkage, ensuring that cracks will not occur even in the presence of a certain temperature gradient. Finally, the shrinkage strain control game model further reduces the risk of early cracking by optimizing curing parameters to maintain surface moisture and reduce internal temperature gradients. Therefore, this invention can effectively solve the problem of early cracking caused by hydration heat temperature rise.

[0060] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0061] The specific implementation method of step S10 is to prepare low-heat, high-crack-resistant concrete. Fly ash is mixed at a dosage of 30-40% with mineral powder at a dosage of 20-30% to form a dual-blending system. Basalt fiber (3-5 kg / m³) and dual-expansion source expansion agent (8-12%) are added. The basalt fiber is pre-dispersed using an airflow dispersion device before being added to the system. The mixture is first dry-mixed for 30 seconds, then wet-mixed for 90 seconds, with the mixing speed controlled at 35-45 rpm. In the dual-blending system, the fly ash consists of spherical glassy particles with pozzolanic activity, participating in the reaction during the later stages of hydration. The mineral powder is finely ground blast furnace slag with potential hydraulic properties. The synergistic effect of both delays the appearance of the peak hydration heat and reduces the peak temperature, lowering the total hydration heat to 60-70% of that of ordinary Portland cement. Basalt fibers are inorganic fibers with a length of 12–18 mm and a diameter of 13–17 micrometers. They possess a tensile strength of 3000–4800 MPa and an elastic modulus of 93–110 GPa. In the concrete matrix, they form a three-dimensional, randomly distributed reinforcing network. Through the frictional interlocking effect between the fibers and the matrix interface, they prevent the propagation of microcracks, thereby increasing the tensile strain capacity of concrete to 180–220 microstrain. The dual-expansion-source expansive agent is composed of calcium oxide and magnesium oxide expansive components. Calcium oxide hydrates to form calcium hydroxide, generating early expansion that compensates for the plastic shrinkage of concrete within 1–3 days. Magnesium oxide hydrates to form magnesium hydroxide, generating later expansion that compensates for the autogenous shrinkage and drying shrinkage of concrete within 7–28 days. The complementary action of the two components achieves a full-age shrinkage compensation effect. The dual-expansion-source expansive agent is formulated with calcium oxide comprising 60–70% of the total expansive agent and magnesium oxide comprising 30–40%. The airflow dispersion device uses high-speed rotating airflow to break down basalt fiber bundles into monofilaments, preventing the fibers from re-agglomerating during concrete mixing. Adding 0.3–0.5% dispersant before feeding improves the wettability of the fiber surface, ensuring a dispersion uniformity coefficient of over 0.85 in the matrix. The dispersion uniformity coefficient is calculated as follows:

[0062] ;

[0063] In the formula, The dispersion uniformity coefficient is dimensionless. The coefficient of variation is dimensionless and is calculated as follows:

[0064] ;

[0065] In the formula, The standard deviation of the number of fibers in the 25 sub-regions, in units of fibers; This represents the average number of fibers across 25 sub-regions, expressed in fibers. Standard deviation. The calculation method is as follows:

[0066] ;

[0067] In the formula, For the first The number of fibers in each subregion, expressed in units of fibers. Values ​​range from 1 to 25; summation symbol This indicates that the summation is performed over all 25 sub-regions. Average value. The calculation method is as follows:

[0068] .

[0069] The dispersion uniformity coefficient was determined using image analysis. The method involved cutting a 100mm × 100mm cross-section from a concrete specimen, capturing the cross-sectional image with a high-resolution camera, and then performing grayscale processing. The cross-section was divided into 25 sub-regions of 20mm × 20mm, and the number of fibers in each sub-region was counted. In the dry-mixing-wet-mixing process, the dry-mixing stage thoroughly mixed cement, fly ash, mineral powder, and sand and gravel aggregates to form a uniform dry material. In the wet-mixing stage, water and admixtures were added, and stirring continued to disperse the basalt fibers in the flowing slurry. The stirring speed was 35–45 rpm to ensure fiber dispersion while avoiding excessive speed that could cause fiber breakage. The total stirring time was controlled to 120 seconds to prevent fiber damage due to over-mixing. The fiber integrity rate was calculated as follows:

[0070] ;

[0071] In the formula, Fiber integrity rate, in percentages (%) This represents the number of fibers with a length greater than 10 mm in the cross-sectional image, expressed in individual fibers. The total number of fibers in the cross-sectional image is expressed in individual fibers. The time allocation of 30 seconds of dry mixing followed by 90 seconds of wet mixing was determined through a fiber dispersion effect experiment. The experimental method involved setting dry mixing times of 10, 20, 30, 40, and 50 seconds, and corresponding wet mixing times of 110, 100, 90, 80, and 70 seconds, while maintaining a total mixing time of 120 seconds. Three sets of concrete test blocks were prepared for each time combination, and the dispersion uniformity coefficient and fiber integrity rate of each set were measured. Based on the experimental data analysis, the time allocation with a dispersion uniformity coefficient greater than 0.85 and a fiber integrity rate greater than 90% was selected as the optimal parameter. The dosage of the dual-expansion-source expansive agent was determined through a concrete expansion rate experiment. The shrinkage compensation rate was calculated based on the experimental data for each dosage. The shrinkage compensation rate was calculated as follows:

[0072] ;

[0073] In the formula, The shrinkage compensation rate is expressed in % (%). The concrete expansion rate is expressed as a percentage and is calculated by measuring the change in length of the prism specimen. This represents the concrete shrinkage rate, expressed as a percentage, obtained by measuring the shrinkage rate at the same age.

[0074] The specific implementation of step S20 involves pre-embedding high-density polyethylene cooling water pipes with a diameter of 25-32mm in a staggered pattern with a horizontal spacing of 600-800mm and a vertical spacing of 500-700mm in the concrete pouring area of ​​the tunnel. A set of temperature sensors is installed every 1500-2000mm. The core temperature sensor is embedded at the center of the concrete cross-section, and the surface temperature sensor is embedded at a depth of 50-80mm from the surface. Platinum resistance temperature sensors with an accuracy of ±0.2℃ are used. Temperature data is collected every 15 minutes and transmitted wirelessly to a monitoring terminal in real time to form a temperature history curve. Based on the core temperature rise rate and the core-to-surface temperature difference, the required cooling water flow rate is calculated using the hydration heat temperature rise control equation. The hydration heat temperature rise control equation is specifically expressed as follows:

[0075] ;

[0076] In the formula, The actual required cooling water flow rate, in units of ; The baseline cooling water flow rate is 10. ; This is the cooling water flow rate adjustment coefficient, dimensionless, and calculated as follows:

[0077] ;

[0078] In the formula, This is the baseline adjustment factor, dimensionless, and defaults to 1.0. The rate of temperature rise in the concrete core, expressed in °C. The temperature is calculated by monitoring data from a temperature sensor. The calculation method is to divide the change in core temperature within the time interval between two adjacent measurements by the time interval. Standard temperature rise rate, unit: °C The empirical value, determined through temperature control experiments, is 2.0℃. ; The temperature difference between the core and the surface is expressed in °C and is obtained by subtracting the surface temperature sensor reading from the core temperature sensor reading. The standard temperature difference, in °C, is calibrated through temperature control experiments, with an empirical value of 20 °C. Indices 0.6 and 0.4 represent the weighting coefficients for the influence of the core temperature rise rate and the core surface temperature difference on the cooling water flow rate adjustment coefficient, respectively, determined through multivariate nonlinear regression analysis. When the core temperature rise rate exceeds 1.8 °C per hour, the water flow rate is increased; when the core surface temperature difference approaches 18 °C, an early warning is activated and the pouring speed is reduced. The cooling water inlet temperature is controlled between 15 and 20 °C, and the flow rate is dynamically adjusted within a range of 8 to 15 °C based on temperature monitoring data and the hydration heat temperature rise control equation. The cyclic cooling duration is 5–7 days after concrete pouring. The arrangement parameters of 600–800 mm horizontal spacing and 500–700 mm vertical spacing were determined through numerical simulation experiments of the concrete temperature field. The experimental method involved establishing a three-dimensional finite element model of the tunnel concrete, setting different combinations of cooling water pipe spacing to conduct transient temperature field analysis, simulating the temperature distribution within 168 hours after pouring, and calculating the maximum core temperature and maximum temperature gradient for each combination. The maximum temperature gradient was calculated as follows:

[0079] ;

[0080] In the formula, Maximum temperature gradient, in °C ; This represents the maximum temperature difference between two adjacent points inside the concrete, expressed in °C, and is calculated using a finite element model. The distance between two adjacent points, in units of The value is usually 1. The spacing combination with a maximum core temperature below 70℃ and a maximum temperature gradient of less than 15℃ per meter was selected as the layout parameters for the cooling water pipe network.

[0081] The specific implementation of step S30 involves using a 3D laser scanner to perform a comprehensive scan of the installed template. The scanning accuracy is ±1mm, and the point cloud density is 2-3mm. A multi-site scanning method is used to ensure that there are no blind spots on the inner surface of the cavity. The point cloud data obtained from the scan is transformed and stitched to form a complete 3D model. Based on the distribution law of concrete lateral pressure and template stiffness parameters, a template deformation prediction equation is established to calculate the expected deformation of each part of the template during the pouring process. Reverse compensation is reserved at each measuring point position during template installation. The template deformation prediction equation is specifically expressed as follows:

[0082] ;

[0083] In the formula, The expected deformation of the template measuring point is expressed in mm. The deformation reference value is in mm, calibrated through template deformation experiments, and the empirical value is 3.0 mm. The height of the concrete pouring is expressed in units of 1. ; Standard pouring height, unit: The empirical value is 5.0, determined through template deformation experiments. ; The unit is the pouring speed. ; Standard pouring speed, unit: The empirical value was determined through template deformation experiments to be 2.5. ; The pouring height influence index is dimensionless and represents the degree of influence of pouring height on formwork deformation. It is determined through multivariate nonlinear regression analysis, with an empirical value of 1.5. The pouring speed influence index is dimensionless and represents the degree of influence of pouring speed on formwork deformation. It is determined through multivariate nonlinear regression analysis, with an empirical value of 0.5. The expected deformation of the formwork measuring points is used to determine the value of the reverse allowance compensation. During formwork installation, reverse allowances are made at each measuring point location, and the compensation amount is equal to the negative of the expected deformation.

[0084] The specific implementation of step S40 is as follows: Concrete is poured in layers of 300-500mm thickness, using a fast-insertion, slow-withdrawal vibration process. The vibrator is inserted into the concrete at a speed of 50-70mm per second to a depth of 50-100mm in the next layer, held for 10-15 seconds, and then slowly withdrawn at a speed of 20-30mm per second. The spacing between insertion points is controlled within 1.5 times the radius of action of the vibrator. An attached vibrator is fixed to the outside of the formwork and vibrates at a frequency of 40-50Hz. The pouring speed is controlled at 2-3... Every hour, the cooling water flow rate is adjusted in real time based on the temperature monitoring data and the calculation results of the hydration heat temperature rise control equation in step S20. The adjustment method is the same as the cooling water flow rate calculation method described in step S20, using the formula... The actual required cooling water flow rate is calculated, and based on the core temperature rise rate... Temperature difference between the core and the surface layer Dynamically adjust the cooling water flow rate adjustment coefficient The parameters for the insertion speed of 50–70 mm / s and the withdrawal speed of 20–30 mm / s in the fast insertion and slow withdrawal vibration compaction process were determined through a concrete air bubble rate experiment. The experimental method involved pouring concrete inside a transparent template and using different combinations of vibrator insertion and withdrawal speeds for compaction. Immediately after compaction, photographs of the template surface were taken, and the number and area ratio of air bubbles with a diameter greater than 2 mm were statistically analyzed using image processing software. The vibration parameter combination with an air bubble area ratio of less than 0.5% was selected as the optimal operating parameters.

[0085] The specific implementation of step S50 is as follows: Immediately after the concrete has set, spray curing begins to keep the surface moist. After 6 hours, a moisturizing curing material is applied and curing continues for at least 14 days. During the curing period, resistance strain gauges are used every 8 hours to measure the shrinkage strain on the concrete surface. The gauge length of the strain gauge is 100 mm, and the sensitivity coefficient is 2.0 ± 0.01. The gauges are attached to the concrete surface at the location directly in contact with the curing material. Strain data is collected every 8 hours to calculate the cumulative strain value. When the cumulative strain exceeds 200 microstrains, the adjustment scheme output by the shrinkage strain control game model is executed. The shrinkage strain control game model includes an upper-level model that aims to maximize the uniformity of concrete surface humidity and a lower-level model that aims to minimize the internal temperature gradient of the concrete. The objective function of the upper-level model is used to optimize the adjustment amount of the spraying frequency, specifically expressed as follows:

[0086] ;

[0087] In the formula, , is a dimensionless index of surface humidity uniformity; The baseline uniformity index is dimensionless and was calibrated through maintenance effect experiments; its empirical value is 0.95. The cumulative strain value, in microstrain, is obtained by summing up the data monitored by strain gauges. The critical strain value is expressed in microstrain and is determined through curing effect experiments. The empirical value is 250 microstrains. This is the amount of spray frequency adjustment, measured in sprays. ; Standard spray frequency, unit: spray times. Through maintenance effect experiments, the empirical value is 0.5 times. ; Function term This represents the influence of the ratio of the spray frequency adjustment to the standard spray frequency on the surface humidity uniformity index. The square root form is used to represent the diminishing marginal effect, meaning that as the spray frequency adjustment increases, its influence on the surface humidity uniformity index gradually weakens. The upper-level model constraints are as follows: Second-rate The objective function of the lower-level model is used to optimize the adjustment of the cover material thickness, and is specifically expressed as follows:

[0088] ;

[0089] In the formula, The temperature gradient control exponent is dimensionless. The baseline gradient index is dimensionless and was calibrated through maintenance effect experiments; its empirical value is 0.90. The temperature difference between the core and the surface is expressed in °C and is obtained by subtracting the surface temperature sensor reading from the core temperature sensor reading. This is the critical temperature difference, in °C, determined through curing effect experiments; the empirical value is 20 °C. The amount of adjustment for the thickness of the covering material is in mm; The standard coverage thickness is in mm, calibrated through curing effect experiments, with an empirical value of 10 mm; function term This represents the influence of the ratio of the adjustment amount of the cover material thickness to the standard cover thickness on the temperature gradient control index. The square root form is used to represent the diminishing marginal effect, meaning that as the adjustment amount of the cover material thickness increases, its influence on the temperature gradient control index gradually weakens. The lower-level model constraints are as follows: mm. The coupling terms between the objective function of the upper-level model and the objective function of the lower-level model are specifically represented as follows:

[0090] ;

[0091] In the formula, This is a coupling term, dimensionless; The coupling standard value, in microstrain·℃, was calibrated through curing effect experiments, with an empirical value of 5000 microstrain·℃; coupling term This demonstrates the interaction between surface shrinkage strain and internal temperature gradient, when the cumulative strain value... Temperature difference between the core and the surface layer When both are increased, the coupling term increases, indicating that both the spray frequency and the thickness of the covering material need to be adjusted simultaneously to achieve a synergistic control effect. The surface humidity uniformity index is obtained by solving the shrinkage strain control game model. and temperature gradient control index At the same time, achieve the optimal spray frequency adjustment. and the amount of adjustment of the thickness of the covering material The solution method employs a multi-objective optimization algorithm to maximize the comprehensive objective function while satisfying the constraints of both the upper-level and lower-level models. The comprehensive objective function is calculated as follows:

[0092] ;

[0093] In the formula, The value of the comprehensive objective function is dimensionless. , is the weighting coefficient for the surface humidity uniformity index, dimensionless, with an empirical value of 0.4; The weighting coefficient for the temperature gradient control index is dimensionless and has an empirical value of 0.4. The coefficient for the coupling term is dimensionless and has an empirical value of 0.2; the coupling term... The use of a negative sign in the overall objective function indicates that the product of the cumulative strain value and the core-surface temperature difference needs to be controlled within a reasonable range to avoid both surface shrinkage strain and internal temperature gradient being too large simultaneously. When the temperature difference between the core and surface drops below 15°C, the cooling water circulation intensity is gradually reduced.

[0094] The specific implementation of step S60 involves using a 3D laser scanner and a total station to jointly measure the actual surface coordinates of the tunnel interior. The total station has a measurement accuracy of ±1mm. A control point network is set up inside the tunnel as a measurement benchmark. For key areas that the laser scanner cannot accurately measure, the total station is used for supplementary measurements. The two sets of measurement data are fused to form high-precision surface data. The measurement data is compared and analyzed with the designed surface coordinates to establish a deviation distribution map. The deviation distribution map displays the spatial distribution characteristics of different deviation intervals in a chromatogram format, identifying the location and pattern of deviations exceeding ±1.5mm. The deviation data is fed back to the template deformation prediction equation in step S30 for parameter correction. The calculation method for the template stiffness parameter correction is as follows:

[0095] ;

[0096] In the formula, This is the correction amount for the template stiffness parameter, in GPa. The measured deviation value is in mm and is determined by the normal deviation between the actual surface coordinates and the designed surface coordinates. The predicted deviation value, in mm, is calculated from the template deformation prediction equation. This is the template stiffness sensitivity coefficient, in units of... The values ​​were obtained through finite element analysis, and the range was 0.8–1.2. , representing the partial derivative of the template deformation with respect to the template stiffness parameter, is calculated by taking the partial derivative of the template stiffness parameter in the template deformation prediction equation. The boundary constraint correction is determined by the difference between the measured support point displacement and the predicted support point displacement; the correction is calculated as follows:

[0097] ;

[0098] In the formula, This represents the correction amount for boundary constraints, in mm. The measured displacement of the support point is in mm and was obtained using a total station. The displacement of the support points, in mm, was calculated using the finite element method. The corrected template stiffness parameters are shown below. and the modified boundary constraints The calculation method is as follows:

[0099] ;

[0100] ;

[0101] In the formula, The template stiffness parameter before correction is expressed in GPa, and the initial template stiffness parameter value used in the template deformation prediction equation is expressed as follows: The first set of boundary constraints is in mm, representing the initial boundary constraint values ​​used in the template deformation prediction equation. The second set of template stiffness parameters is after correction. and boundary constraints Substitute the template deformation prediction equation into the equation and recalculate the expected deformation and reverse reserved compensation of each measuring point in the next tunnel section to achieve closed-loop control and continuous optimization based on measured data.

[0102] It should be noted that the variables involved in this embodiment are explained in detail in Table 1.

[0103] Table 1. Variable Explanation Table

[0104]

[0105] To better understand and implement this invention, a specific application scenario, Example 2, is provided below: First, low-heat, high-crack-resistant concrete is prepared. The technical team selected P.O 42.5 grade ordinary Portland cement as the cementitious material, forming a dual-blending system with a fly ash content of 35% and a mineral powder content of 25%. The fly ash used... Grade A fly ash, its The content is 52%, and the mineral powder is selected. Grade 1 slag powder with a specific surface area of ​​420 Preliminary hydration heat experiments verified that the peak temperature rise of the concrete with this mix proportion was 51℃ within 72 hours, with the peak time delayed to 26 hours, and the total hydration heat reduced to 65% of that of standard Portland cement. Basalt fiber uses short-cut fibers with a length of 15mm and a diameter of 15μm, with a dosage of 4%. The tensile strength is 3800 MPa, and the elastic modulus is 100 GPa. The dual-expansion source expansion agent dosage is 10%, with calcium oxide comprising 65% and magnesium oxide 35%. Before concrete mixing, the technical team used an airflow dispersion device to pre-disperse the basalt fibers, while adding 0.4% polycarboxylate dispersant to improve fiber surface wettability. The mixing process employed a dry-mixing process for 30 seconds followed by wet-mixing for 90 seconds, with the mixing speed controlled at 40 rpm to ensure uniform fiber dispersion in the matrix. Cross-sectional image analysis showed a fiber dispersion uniformity coefficient of 0.87 and a fiber integrity rate of 92%.

[0106] The pre-installation of the cooling water pipe network was completed before the formwork installation. The technical team used 28mm diameter high-density polyethylene pipes, laid in a staggered pattern with a horizontal spacing of 700mm and a vertical spacing of 600mm within the concrete pouring area of ​​the tunnel. A total of eight layers of cooling water pipes were laid, each layer approximately 180 meters long, forming a cooling network covering the entire tunnel cross-section. Temperature monitoring points were set every 1800mm, with a total of 25 temperature sensors. Each set included a core temperature sensor and a surface temperature sensor. The core temperature sensor was embedded at the center of the concrete cross-section, and the surface temperature sensor was embedded at a depth of 65mm from the surface. All temperature sensors were platinum resistance temperature sensors with an accuracy of ±0.2℃ and a data acquisition interval of 15 minutes. The technical team established a temperature data acquisition system, transmitting monitoring data wirelessly to a monitoring terminal in real time to generate temperature history curves for continuous monitoring of internal concrete temperature changes.

[0107] The pre-deformation compensation design of the formwork is a crucial step in ensuring the accuracy of the tunnel's surface profile. The technical team used a 3D laser scanner to comprehensively scan the installed steel formwork, achieving a scanning accuracy of ±1mm and a point cloud density of 2.5mm. The scanning employed a multi-site scanning method with eight stations to ensure no blind spots on the tunnel's inner surface. The obtained point cloud data, after coordinate transformation and stitching, formed a complete 3D model of the formwork, yielding initial surface coordinate data for approximately 5.8 million measuring points. Based on the distribution law of concrete lateral pressure and the formwork stiffness parameters, the technical team established a formwork deformation prediction equation. The formwork was constructed using 12mm thick steel plates with an elastic modulus of 206GPa and a Poisson's ratio of 0.3, reinforced at the back with steel ribs spaced 800mm apart. Finite element analysis calculated the formwork stiffness sensitivity coefficient to be 1.0mm / GPa. Based on the planned pouring height of 9 meters and a pouring speed of 2.5 meters per hour, the formwork deformation prediction equation calculated the expected deformation at each measuring point, such as... Figure 2 As shown. The expected maximum deformation of the bottom formwork of the tunnel is 4.2mm, the middle is 3.1mm, and the top is 1.8mm. The technical team made reverse allowances for compensation at each measuring point during formwork installation, with a 4.2mm inward offset at the bottom, a 3.1mm offset at the middle, and a 1.8mm offset at the top, to offset the formwork deformation during the pouring process.

[0108] Concrete pouring was carried out in layers. Each layer was 400mm thick, starting from the bottom of the cavity and progressing upwards. The pouring speed was strictly controlled at 2.5 meters per hour to avoid excessive lateral pressure and potential deformation of the formwork due to excessive pouring speed. The vibration process employed a quick-insertion, slow-withdrawal method. The vibrator had a diameter of 50mm, an insertion speed of 60mm / s, and was inserted to a depth of 80mm into the next layer of concrete, held for 12 seconds, and then slowly withdrawn at 25mm / s. The insertion point spacing was controlled at 500mm to ensure sufficient coverage of the effective vibration range. Simultaneously, an attached vibrator was fixed to the outside of the formwork, operating at a frequency of 45Hz to promote concrete compaction and air release, preventing surface air bubbles. During pouring, a temperature monitoring system continuously recorded changes in the internal temperature of the concrete. Eight hours after pouring began, the core temperature began to rise rapidly, reaching a rate of 1.5℃ / h. Based on the hydration heat temperature rise control equation, the cooling water flow rate adjustment coefficient was calculated to be 0.84, and the actual required cooling water flow rate was 8.4. / h. 18 hours after casting, the core temperature reached its peak of 68℃, with a temperature difference of 16℃ between the core and the surface. At this point, the temperature rise rate decreased to 0.8℃ / h, the cooling water flow rate adjustment coefficient decreased to 0.52, and the cooling water flow rate was adjusted to 5.2. The rate of temperature increase in the core area was 1.9℃ / h, as shown in Table 2. Thirty-six hours after casting, the core temperature rose again to 1.9℃ / h, and the temperature difference between the core and surface reached 17.5℃, approaching the warning threshold of 18℃. The technical team immediately increased the cooling water flow rate to 12.5℃ / h. / h, effectively controlling the temperature from continuing to rise.

[0109] Table 2. Temperature monitoring and cooling water flow control data during the pouring process

[0110]

[0111] Six hours after the concrete reached its final set, the technical team immediately began early curing. Initially, spray curing was used to keep the surface moist, with a spray frequency of 0.3 times per hour and each spray lasting 5 minutes. After 6 hours, a 10mm thick moisture-retaining curing blanket was applied, and curing continued for 14 days. During the curing period, the technical team measured the surface shrinkage strain of the concrete every 8 hours using resistance strain gauges. The gauge length of the strain gauges was 100mm, and the sensitivity coefficient was 2.0. At 24 hours after the start of curing, the cumulative strain reached 85 microstrains; at 48 hours, the cumulative strain reached 156 microstrains; and at 72 hours, the cumulative strain reached 218 microstrains, exceeding the threshold of 200 microstrains. At this point, the temperature difference between the core and the surface was 11℃, and the technical team activated a shrinkage strain control game model to adjust the curing parameters. The objective function of the upper layer of the model calculated the surface humidity uniformity index. Inputting the cumulative strain value of 218 microstrains and the current spray frequency of 0.3 times / h, the calculated adjustment amount for the spray frequency was 0.35 times / h, increasing the spray frequency to 0.65 times / h after the adjustment. The lower-level objective function of the model calculates the temperature gradient control index. Inputting a core-to-surface temperature difference of 11℃ and a current covering material thickness of 10mm, the calculated adjustment to the covering material thickness is 8mm, increasing the total covering material thickness to 18mm. After 48 hours of the adjusted curing regimen, the cumulative strain growth rate decreased from 62 microstrain increases per 8 hours to 23 microstrain increases per 8 hours, indicating that the adjustment effectively controlled surface shrinkage. During the curing process, the core-to-surface temperature difference gradually decreased. When the temperature difference dropped below 14℃, the technical team gradually reduced the cooling water circulation intensity, decreasing the cooling water flow rate from 3.5... / h decreased to 1.8 / h, stop cooling water circulation on the 7th day of maintenance.

[0112] After 14 days of curing, the formwork was removed, and the technical team immediately conducted precise measurements. A 3D laser scanner and a total station were used together to measure the actual surface coordinates of the tunnel interior. The laser scanner obtained approximately 6.2 million measurement points, while the total station supplemented the measurements at 86 control points in key areas. The measurement data were compared and analyzed with the designed surface coordinates to create a deviation distribution map, such as... Figure 3As shown in the deviation distribution diagram, 95% of the area on the inner surface of the cavity has a deviation within ±1.2mm, while the area with a deviation exceeding ±1.5mm accounts for only 2.3%, mainly distributed at the bottom corners and the top center of the cavity. The deviation at the bottom corners is +1.7mm, and the deviation at the top center is -1.6mm. The technical team analyzed the deviation data and found that the deviation at the bottom corners was due to insufficient template stiffness at that location, resulting in actual deformation exceeding the predicted deformation. The deviation at the top center was due to insufficient consideration of the displacement of the support points. The technical team fed the deviation data back into the template deformation prediction equation for parameter correction. Using the least squares method, they corrected the template stiffness parameters. The template stiffness parameter at the bottom corners was corrected from the original 206GPa to 188GPa, and the constraint stiffness of the top support points was corrected from the original fully fixed setting to allow for 0.5mm displacement. The revised template deformation prediction equation calculates the expected deformation at each measuring point in the next tunnel section, improving the prediction accuracy from ±0.8mm to ±0.3mm. This provides more accurate template pre-deformation compensation data for subsequent tunnel section construction. Figure 4 As shown.

[0113] This invention improves the crack resistance of concrete by reducing the heat of hydration and delaying the peak exothermic effect through a dual-blending system, combined with a basalt fiber three-dimensional reinforcement network to prevent microcrack propagation, and full-age shrinkage compensation by a dual-expansion-source expansion agent. An embedded cooling water pipe network, combined with temperature monitoring and the hydration heat temperature rise control equation, enables active temperature field regulation, avoiding temperature stress concentration. The pre-deformation compensation design of the formwork, based on three-dimensional laser scanning and deformation prediction equations, transforms passive deformation acceptance into active pre-compensation, eliminating the impact of formwork deformation on surface accuracy. Layered pouring combined with a fast-insertion, slow-extraction vibration process ensures concrete density and surface quality. Early curing uses a shrinkage strain control game model to dynamically optimize spraying frequency and covering material thickness, achieving coordinated control of surface humidity uniformity and internal temperature gradient. Precision measurement and deviation correction after demolding form a closed-loop control, feeding measured data back to the prediction model for parameter optimization, continuously improving prediction accuracy. These technical measures work together to form a complete crack-resistant construction technology system, encompassing material preparation, temperature control, template deformation control, vibration process, curing regulation, and measurement feedback, achieving the goal of crack-free construction of high-precision fair-faced concrete for wind tunnel bodies.

[0114] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a cast-in-place fair-faced concrete wind tunnel with crack resistance, characterized in that, This includes preparing low-heat, high-crack-resistant concrete by mixing fly ash and mineral powder to form a dual-blending system, adding basalt fiber and dual-expansion-source expansion agent, using an airflow dispersion device to pre-disperse the basalt fiber before feeding it into the concrete, and mixing it dry first and then wet; pre-burying cooling water pipes and setting up temperature monitoring points, laying a cooling water pipe network in the concrete pouring area of ​​the tunnel, setting up temperature sensors to monitor the core temperature and surface temperature of the concrete respectively, establishing a temperature data acquisition system to record the temperature change curve during the pouring process in real time, and calculating the required cooling water flow rate through the hydration heat temperature rise control equation; The template pre-deformation compensation design is carried out. A three-dimensional laser scanner is used to scan the installed template to obtain the initial surface coordinate data. Based on the concrete lateral pressure distribution law and template stiffness parameters, the template deformation prediction equation is established to calculate the expected deformation of each part of the template during the pouring process. Reverse compensation is reserved at each measuring point position during template installation. Concrete is poured in layers with dynamic temperature rise rate control. A fast-insertion, slow-withdrawal vibration process is used in conjunction with an attached vibrator to remove air bubbles. Cooling water flow is adjusted in real time based on temperature monitoring data and the hydration heat temperature rise control equation. Early curing is implemented and shrinkage deformation is monitored. Spray curing is started immediately after the concrete has set to keep the surface moist. Moisturizing curing material is used for continuous curing. During the curing period, strain gauges are used to measure the shrinkage strain on the concrete surface. The adjustment amount of spray frequency and covering material thickness is calculated through a shrinkage strain control game model. After demolding, precise measurements and deviation corrections are performed. A three-dimensional laser scanner and a total station are used to measure the actual surface coordinates of the tunnel interior. The measured data are compared and analyzed with the designed surface coordinates to establish a deviation distribution map. The deviation data is then fed back to the template deformation prediction equation for parameter correction.

2. The method according to claim 1, characterized in that, The dual-blending system is specifically formed by mixing fly ash at a dosage of 30 to 40% and mineral powder at a dosage of 20 to 30%.

3. The method according to claim 2, characterized in that, The basalt fibers are specifically 12 to 18 mm in length and 13 to 17 mm in diameter. Inorganic fibers, with a doping amount of 3 to 5 .

4. The method according to claim 3, characterized in that, The dual-expansion source expansion agent is composed of calcium oxide expansion component and magnesium oxide expansion component, with a dosage of 8% to 12%.

5. The method according to claim 4, characterized in that, The process of first dry mixing and then wet mixing specifically involves dry mixing for 30 seconds followed by wet mixing for 90 seconds, with the mixing speed controlled at 35 to 45 revolutions per minute.

6. The method according to claim 5, characterized in that, The cooling water pipe network is pre-embedded in the tunnel concrete in a quincunx pattern with a horizontal spacing of 600 to 800 mm and a vertical spacing of 500 to 700 mm.

7. The method according to claim 6, characterized in that, The temperature sensors are set up every 1500 to 2000 mm. The core temperature sensor is embedded in the center of the concrete section, and the surface temperature sensor is embedded at a depth of 50 to 80 mm from the surface.

8. The method according to claim 7, characterized in that, The hydration heat temperature rise control equation is specifically: the cooling water flow rate adjustment coefficient is equal to the base adjustment coefficient multiplied by the ratio of the core temperature rise rate to the standard temperature rise rate to the power of 0.6 multiplied by the ratio of the core surface temperature difference to the standard temperature difference to the power of 0.

4.

9. The method according to claim 8, characterized in that, The template deformation prediction equation is specifically: the expected deformation at the template measuring point is equal to the 1.5th power of the ratio of the deformation benchmark value multiplied by the pouring height divided by the standard pouring height multiplied by the 0.5th power of the ratio of the pouring speed divided by the standard pouring speed.

10. The method according to claim 9, characterized in that, The layered concrete pouring specifically involves pouring concrete in layers with a thickness of 300 to 500 mm, with the pouring speed controlled at 2 to 3 meters per hour.

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